Parameter Identification of a Dynamic Model of Cultures of Microalgae Scenedesmus obliquus- An experimental study

A robust multinutrient kinetic model for enhanced lutein and biomass yields in mixotrophic microalgae cultivation: A step towards successful large-scale productions

Mixotrophic cultivation holds great promise to significantly enhance the productivities of biomass and valuable metabolites from microalgae. In this study, a new kinetic model is developed, explicitly describing the effect of the most influential environmental factors on both biomass growth and the production of the high-value product lutein. This extensive study of multinutrient kinetics for Tetradesmus obliquus in a mixotrophic regime covers various nutritional conditions. Crucial nutrients governing the model include nitrate, phosphate, and glucose. Using seven state variables and 13 unknown parameters, the model's accuracy was ensured through a well-designed two-factor, four-level experimental setup, providing ample data for reliable calibration and validation. Results accurately predict dynamic concentration profiles for all validation experiments, revealing broad applicability. Optimizing nitrogen availability led to significant increases in biomass (up to fourfold) and lutein production (up to 12-fold), with observed maximum biomass concentration of 6.80 g L-1 and lutein reaching 25.58 mg L-1. Noticeably, the model exhibits a maximum specific growth rate of 4.03 day-1, surpassing reported values for photoautotrophic and heterotrophic conditions, suggesting synergistic effects. Valuable guidance is provided for applying the method to various microalgal species and results are large-scale production-ready. Future work will exploit these results to develop real-time photobioreactor operation strategies.

A reliable multi-nutrient model for the rapid production of high-density microalgal biomass over a broad spectrum of mixotrophic conditions

The superior microalgal biomass productivities obtained under mixotrophic conditions have been widely demonstrated. However, to attain the full potential of the method, optimal conditions for biomass production and resource utilization need to be determined and successfully exploited throughout the process operation. Detailed kinetic mathematical models have often proved most efficient tools for predicting process behavior and governing its overall operation. This paper presents an extensive study for obtaining a highly reliable model for mixotrophic production of microalgae covering a wide set and range of nutritional conditions (10-fold the concentration range of Bold's Basal Medium) and biomass yields up to 6.68 g.L^-1 after only 6 days. The final reduced model includes a total of five state variables and nine parameters: model calibration resulted in very small 95% confidence intervals and relative errors below 5% for all parameters. Model validation showed high reliability with R² correlation values between 0.77 and 0.99.

Photosynthetic Carbon Uptake Correlates with Cell Protein Content during Lipid Accumulation in the Microalga Chlorella vulgaris NIES 227

Large-scale microalgae cultivation for biofuel production is currently limited by the possibility of maintaining high microalgae yield and high lipid content, concomitantly. In this study, the physiological changes of Chlorella vulgaris NIES 227 during lipid accumulation under nutrient limitation was monitored in parallel with the photosynthetic capacity of the microalgae to fix carbon from the proxy of oxygen productivity. In the exponential growth phase, as the biomass composition did not vary significantly (approx. 53.6 ± 7.8% protein, 6.64 ± 3.73% total lipids, and 26.0 ± 9.2% total carbohydrates of the total biomass dry-weight), the growth capacity of the microalgae was preserved (with net O2 productivity remaining above (4.44 ± 0.93) × 10−7 g O2·µmol PAR−1). Under nutrient limitation, protein content decreased (minimum of approx. 18.6 ± 6.0%), and lipid content increased (lipid content up to 56.0 ± 0.8%). The physiological change of the microalgae was associated with a loss of photosynthetic activity, down to a minimum (1.27 ± 0.26) × 10−7 g O2·µmol PAR−1. The decrease in photosynthetic O2 productivity was evidenced to correlate to the cell internal-protein content (R2 = 0.632, p = 2.04 × 10−6, N = 25). This approach could serve to develop productivity models, with the aim of optimizing industrial processes.

A scalable model for EPA and fatty acid production by Phaeodactylum tricornutum

Large-scale photoautotrophic production of microalgae has the potential to provide a sustainable supply of omega-3 fatty acids (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) for human and animal nutrition. This study presents a kinetic model for the EPA-producing microalga Phaeodactylum tricornutum in photoautotrophic conditions, with light and nitrogen being the growth limiting factors. The model was developed using a dataset obtained from bench-scale (5 L) cultures and was successfully validated against pilot-scale (50 L) cultures. This model is the first to predict the biomass and total fatty acid accumulation along with the EPA concentrations in the biomass and total fatty acid fraction for microalgae. The model was used to develop an optimized repeated-batch strategy; implementation of this led to increases in the biomass and EPA productivities of 50 and 20% respectively. This clearly indicates the potential of the model to be used as a tool in the design, optimization and scale-up of microalgal systems for EPA production.

Experimental Study of Substrate Limitation and Light Acclimation in Cultures of the Microalgae Scenedesmus obliquus—Parameter Identification and Model Predictive Control

In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.

Models of microalgal cultivation for added-value products - A review

Microalgae are considered a promising feedstock for biorefineries given that their chemical composition - rich in carbohydrate and lipid - can be directed towards the co-production of various value-added fuels and chemicals. Production of microalgal biomass for biorefinery purposes requires the identification and establishment of optimal cultivation systems, a crucial yet complicated task due to the numerous factors (e.g. media composition, light, temperature) that simultaneously regulate biomass growth and intracellular composition. Modelling these biological processes, taking into account a single or multiple growth-limiting factors, offers a valuable tool to simulate, design and optimise the dynamics of microalgae cultivation. This review provides an overview of existing models developed to describe microalgal growth processes at the macroscopic scale (also termed black-box models) and discusses their formulation in detail. The black-box kinetic modelling frameworks are compiled into single-factor (6 formulations) and multiple-factor (32 formulations - further divided into non-interactive, additive, and interactive) growth kinetic models, as reported in more than 80 studies, for the prediction of biomass growth as a function of major operational factors such as media composition (e.g. nutrient concentration) and environmental factors (e.g. transient light and temperature). In addition, the review focuses on those models that further account for the production dynamics of two microalgal intracellular products with renowned potential as biorefinery substrates: carbohydrate and lipid molecules. Models of microalgal cultivation dynamics offer a robust engineering tool to understand the natural yet complex responses of microalgae to their growing environment and can help - if used appropriately - to optimise microalgae cultivation and increase the economic viability and sustainability of microalgal systems.